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1.
Am J Med Genet A ; 185(1): 242-249, 2021 01.
Artículo en Inglés | MEDLINE | ID: mdl-33098373

RESUMEN

Williams-Beurens syndrome (WBS) is a rare genetic disorder caused by a recurrent 7q11.23 microdeletion. Clinical characteristics include typical facial dysmorphisms, weakness of connective tissue, short stature, mild to moderate intellectual disability and distinct behavioral phenotype. Cardiovascular diseases are common due to haploinsufficiency of ELN gene. A few cases of larger or smaller deletions have been reported spanning towards the centromeric or the telomeric regions, most of which included ELN gene. We report on three patients from two unrelated families, presenting with distinctive WBS features, harboring an atypical distal deletion excluding ELN gene. Our study supports a critical role of CLIP2, GTF2IRD1, and GTF2I gene in the WBS neurobehavioral profile and in craniofacial features, highlights a possible role of HIP1 in the autism spectrum disorder, and delineates a subgroup of WBS individuals with an atypical distal deletion not associated to an increased risk of cardiovascular defects.


Asunto(s)
Enfermedad Celíaca/genética , Elastina/genética , Trastornos Neurocognitivos/genética , Síndrome de Williams/genética , Adolescente , Adulto , Enfermedad Celíaca/complicaciones , Enfermedad Celíaca/patología , Niño , Deleción Cromosómica , Cromosomas Humanos Par 7/genética , Femenino , Predisposición Genética a la Enfermedad , Haploinsuficiencia/genética , Humanos , Trastornos Neurocognitivos/complicaciones , Trastornos Neurocognitivos/patología , Fenotipo , Síndrome de Williams/complicaciones , Síndrome de Williams/patología
2.
Am J Med Genet A ; 179(8): 1615-1621, 2019 08.
Artículo en Inglés | MEDLINE | ID: mdl-31145527

RESUMEN

Only a few individuals with 12q15 deletion have been described, presenting with a disorder characterized by learning disability, developmental delay, nasal speech, and hypothyroidism. The smallest region of overlap for this syndrome was included in a genomic segment spanning CNOT2, KCNMB4, and PTPRB genes. We report on an additional patient harboring a 12q15 microdeletion encompassing only part of CNOT2 gene, presenting with a spectrum of clinical features overlapping the 12q15 deletion syndrome phenotype. We propose CNOT2 as the phenocritical gene for 12q15 deletion syndrome and its haploinsufficiency being associated with an autosomal dominant disorder, presenting with developmental delay, hypotonia, feeding problems, learning difficulties, nasal speech, skeletal anomalies, and facial dysmorphisms.


Asunto(s)
Deleción Cromosómica , Trastornos de los Cromosomas/diagnóstico , Trastornos de los Cromosomas/genética , Cromosomas Humanos Par 12 , Heterocigoto , Fenotipo , Proteínas Represoras/genética , Eliminación de Secuencia , Facies , Estudios de Asociación Genética , Predisposición Genética a la Enfermedad , Haploinsuficiencia , Humanos
3.
Comput Intell Neurosci ; 2017: 1512670, 2017.
Artículo en Inglés | MEDLINE | ID: mdl-28638405

RESUMEN

The primary cause of injury-related death for the elders is represented by falls. The scientific community devoted them particular attention, since injuries can be limited by an early detection of the event. The solution proposed in this paper is based on a combined One-Class SVM (OCSVM) and template-matching classifier that discriminate human falls from nonfalls in a semisupervised framework. Acoustic signals are captured by means of a Floor Acoustic Sensor; then Mel-Frequency Cepstral Coefficients and Gaussian Mean Supervectors (GMSs) are extracted for the fall/nonfall discrimination. Here we propose a single-sensor two-stage user-aided approach: in the first stage, the OCSVM detects abnormal acoustic events. In the second, the template-matching classifier produces the final decision exploiting a set of template GMSs related to the events marked as false positives by the user. The performance of the algorithm has been evaluated on a corpus containing human falls and nonfall sounds. Compared to the OCSVM only approach, the proposed algorithm improves the performance by 10.14% in clean conditions and 4.84% in noisy conditions. Compared to Popescu and Mahnot (2009) the performance improvement is 19.96% in clean conditions and 8.08% in noisy conditions.


Asunto(s)
Accidentes por Caídas , Acústica , Algoritmos , Máquina de Vectores de Soporte , Accidentes por Caídas/mortalidad , Anciano , Humanos , Distribución Normal
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